A novel active suspension control strategy is introduced to improve dynamic response of vehicle suspension systems. The proposed algorithm is a fusion of classical controller design methods together with an online observer and is based on the cancelation of system disturbances. The operational calculus method and the differential algebraic theory are applied to build the observer/compensator that is appended to the classical linear quadratic regulator. An ultra-local model based on linear algebraic rules is presented avoiding the use of a precise mathematical model while guaranteeing the stability of the overall system. Simplicity of implementation, low power demand and significant enhancement of active suspension performance are the observed features of the proposed controller. The numerical simulations illustrate the effectiveness and the robustness against sprung mass variation of the proposed control method compared to proportional–integral–derivative controller, intelligent proportional–derivative controller, linear quadratic regulator and active disturbance rejection—linear quadratic regulator.
StriberskyAMoserFRulkaW. Structural dynamics and ride comfort of a rail vehicle system. Adv Eng Softw2002; 33: 541–552.
2.
WangYZhaoW ZhouG, et al. Suspension mechanical performance and vehicle ride comfort applying a novel jounce bumper based on negative Poisson’s ratio structure. Adv Eng Softw2018; 122: 1–12.
3.
MustafaGIWangHPTianY. Vibration control of an active vehicle suspension systems using optimized model-free fuzzy logic controller based on time delay estimation. Adv Eng Softw2019; 127: 141–149.
4.
WangHPMustafaGITianY. Model-free fractional-order sliding mode control for an active vehicle suspension system. Adv Eng Softw2018; 115: 452–461.
5.
ZhaoZHeXAhnCK. Boundary disturbance observer-based control of a vibrating single-link flexible manipulator. IEEE T Syst Man Cy: S. Epub ahead of print 7May2019. DOI: 10.1109/TSMC.2019.2912900.
6.
ZhaoZHeXRenZ, et al. Output feedback stabilization for an axially moving system. IEEE T Syst Man Cy: S2018; 49: 2374–2383.
7.
ZhaoZHeXRenZ, et al. Boundary adaptive robust control of a flexible riser system with input nonlinearities. IEEE T Syst Man Cy: S2018; 49: 1971–1980.
8.
HeWHeXSunC. Vibration control of an industrial moving strip in the presence of input deadzone. IEEE T Ind Electron2017; 64: 4680–4689.
9.
PanHSunWGaoH, et al. Nonlinear tracking control based on extended state observer for vehicle active suspensions with performance constraints. Mechatronics2015; 30: 363–370.
10.
HaddarMChaariRBaslamisliSC, et al. Intelligent PD controller design for active suspension system based on robust model-free control strategy. Proc IMechE, Part C: J Mechanical Engineering Science2019; 233: 4863–4880.
11.
Cortés-RomeroJJimenez-TrianaACoral-EnriquezH, et al. Estimation and active disturbance rejection in the control of flat systems. Control Eng Pract2017; 61: 173–182.
12.
GaoZ. Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the American control conference, Denver, CO, 4–6 June 2003, pp.4989–4996. New York: IEEE.
13.
HaddarMBaslamisliSCChaariF, et al. On-line adaptive scaling parameter in active disturbance rejection controller. In: Signal processing applied to rotating machinery diagnostics (SIGPROMD’2017), Setif, 9–11 April 2017, pp.79–86. Cham: Springer.
14.
PusadkarUSChaudhariSDShendgePD, et al. Linear disturbance observer based sliding mode control for active suspension systems with non-ideal actuator. J Sound Vib2019; 442: 428–444.
15.
WangJJinFZhouL, et al. Implementation of model-free motion control for active suspension systems. Mech Syst Signal Pr2019; 119: 589–602.
16.
KaleemullahMFarisWFRashidMM, et al. Comparative analysis of LQR and robust controller for active suspension. Int J Veh Noise Vib2012; 8: 367–386.
17.
GuptaSGinoyaDShendgePD, et al. An inertial delay observer-based sliding mode control for active suspension systems. Proc IMechE, Part D: J Automobile Engineering2016; 230: 352–370.
18.
GraaMNejlaouiMHouidiA, et al. Modeling and control of rail vehicle suspensions: a comparative study based on the passenger comfort. Proc IMechE, Part C: J Mechanical Engineering Science2018; 232: 260–274.
19.
KaleemullahMFarisWFHasbullahF, et al. Control of active suspension system using robust H∞ control with genetic algorithm. Int J Adv Sci Techn2019; 28: 763–782.
20.
FuZJLiBNingXB, et al. Online adaptive optimal control of vehicle active suspension systems using single-network approximate dynamic programming. Math Probl Eng2017; 2017: 4575926.
21.
PrassadGSMohanMK. A contemporary adaptive air suspension using LQR control for passenger vehicles. ISA T2019; 93: 244–254.
22.
BaranJ. Disturbance observer based control of active suspension system with uncertain parameters. Prz Elektrotechniczn2016; 92: 102634.
23.
HaddarMBaslamisliSCChaariR, et al. Road profile identification with an algebraic estimator. Proc IMechE, Part C: J Mechanical Engineering Science2019; 233: 1139–1155.
24.
FliessMJoinC. Model-free control. Int J Control2013; 86: 2228–2252.
25.
KasacJMajeticDBrezakD. An algebraic approach to on-line signal denoising and derivatives estimation. J Frankl Inst2018; 355: 7799–7825.
26.
JudaletVGlaserSBoussardC, et al. Implementation of first order algebraic estimators for numerical filtering and derivation applications. IFAC P Vol2014; 47: 9152–9158.
27.
KimGWKangSWKimJS, et al. Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter. Proc IMechE, Part D: J Automobile Engineering2010; 234: 1610–1622.
28.
ZhengWDanDChengW, et al. Real-time dynamic displacement monitoring with double integration of acceleration based on recursive least squares method. Measurement2019; 141: 460–471.
29.
JazarRN. Quarter car model. In: JazarRN (ed.) Vehicle dynamics. New York: Springer, 2014, pp.985–1026.
30.
RajamaniR. Vehicle dynamics and control. Boston, MA: Springer Science & Business Media, 2011.
31.
DelaleauE. A proof of stability of model-free control. In: 2014 IEEE conference on Norbert Wiener in the 21st century (21CW), Boston, MA, 24–26 June 2014, pp.1–7. New York: IEEE.
32.
FliessMSira-RamirezH. An algebraic framework for linear identification. ESAIM: Contr Op Ca Va2003; 8: 151–168.
33.
YosidaK. Operational calculus: A theory of hyperfunctions. 1st ed. New York: Springer Science & Business Media, 2012, pp.53–73.
34.
MenhourLd’Andréa-NovelBFliessM, et al. A new model-free design for vehicle control and its validation through an advanced simulation platform. In: 2015 European control conference (ECC), Linz, 15–17 July 2015, pp.2114–2119. New York: IEEE.
35.
León-VargasFGarelliFZapateiroM. Limiting vertical acceleration for ride comfort in active suspension systems. Proc IMechE, Part I: J Systems and Control Engineering2018; 232: 223–232.
36.
ISO 8608:1995. Mechanical vibration–road surface profiles–reporting of measured data.
